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      Copper resistance in the cold: Genome analysis and characterisation of a P IB‐1 ATPase in Bizionia argentinensis

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          Abstract

          Copper homeostasis is a fundamental process in organisms, characterised by unique pathways that have evolved to meet specific needs while preserving core resistance mechanisms. While these systems are well‐documented in model bacteria, information on copper resistance in species adapted to cold environments is scarce. This study investigates the potential genes related to copper homeostasis in the genome of Bizionia argentinensis (JUB59‐T), a psychrotolerant bacterium isolated from Antarctic seawater. We identified several genes encoding proteins analogous to those crucial for copper homeostasis, including three sequences of copper‐transport P1B‐type ATPases. One of these, referred to as BaCopA1, was chosen for cloning and expression in Saccharomyces cerevisiae. BaCopA1 was successfully integrated into yeast membranes and subsequently extracted with detergent. The purified BaCopA1 demonstrated the ability to catalyse ATP hydrolysis at low temperatures. Structural models of various BaCopA1 conformations were generated and compared with mesophilic and thermophilic homologous structures. The significant conservation of critical residues and structural similarity among these proteins suggest a shared reaction mechanism for copper transport. This study is the first to report a psychrotolerant P1B‐ATPase that has been expressed and purified in a functional form.

          Abstract

          This study explores copper homeostasis in Bizionia argentinensis, a psychrotolerant bacterium from Antarctic seawater. The genome reveals genes similar to known copper homeostasis proteins, including a P1B‐type ATPase, BaCopA1. Expressed in yeast, BaCopA1 catalyses ATP hydrolysis at low temperatures. Its structural similarity to other proteins suggests a shared copper transport mechanism.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The Phyre2 web portal for protein modeling, prediction and analysis.

            Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
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              ColabFold: making protein folding accessible to all

              ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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                Author and article information

                Contributors
                burgardtnoelia@gmail.com , burgardt@qb.ffyb.uba.ar
                lgf@qb.ffyb.uba.ar
                Journal
                Environ Microbiol Rep
                Environ Microbiol Rep
                10.1111/(ISSN)1758-2229
                EMI4
                Environmental Microbiology Reports
                John Wiley & Sons, Inc. (Hoboken, USA )
                1758-2229
                28 June 2024
                August 2024
                : 16
                : 4 ( doiID: 10.1111/emi4.v16.4 )
                : e13278
                Affiliations
                [ 1 ] Laboratorio de Biofísica Molecular, Facultad de Farmacia y Bioquímica, Instituto de Química y Fisicoquímica Biológicas Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas Buenos Aires Argentina
                [ 2 ]Present address: Departamento de Ciencia y Tecnología Universidad Nacional de Quilmes Bernal Argentina
                Author notes
                [*] [* ] Correspondence

                Noelia I. Burgardt and F. Luis González Flecha, Laboratorio de Biofísica Molecular, Facultad de Farmacia y Bioquímica, Instituto de Química y Fisicoquímica Biológicas, Universidad de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.

                Email: burgardtnoelia@ 123456gmail.com ; burgardt@ 123456qb.ffyb.uba.ar and lgf@ 123456qb.ffyb.uba.ar

                Author information
                https://orcid.org/0000-0003-2828-7907
                Article
                EMI413278
                10.1111/1758-2229.13278
                11213822
                38943264
                6f5e150a-1477-479d-96c1-3dabd1491441
                © 2024 The Authors. Environmental Microbiology Reports published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 February 2024
                : 19 April 2024
                Page count
                Figures: 9, Tables: 3, Pages: 16, Words: 10800
                Funding
                Funded by: Fondo para la Investigación Científica y Tecnológica , doi 10.13039/501100006668;
                Award ID: PICT‐2018‐01026
                Award ID: PICT‐2019‐02768
                Funded by: Consejo Nacional de Investigaciones Científicas y Técnicas , doi 10.13039/501100002923;
                Award ID: PIP 3266CO
                Funded by: Universidad de Buenos Aires , doi 10.13039/501100005363;
                Award ID: UBACyT 226BA
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                August 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.5 mode:remove_FC converted:29.06.2024

                Microbiology & Virology
                Microbiology & Virology

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